Spatial models for scaling optimal nutrient management research from plot to field and watershed scales

Issue

Improved technologies for mapping soil nutrient concentrations and corn nitrogen needs will reduce the uncertainty in the 4Rs of fertilizer application, enhance the analysis of environmental conditions that modify soil nutrient concentrations and potentially lead to spatio-temporal models where soil nutrient levels in the growing season can be predicted from soil sampling prior to planting. However, the information learned from research needs to be “scaled up” for applicability to areas beyond the plot scale. This process of extending information to areas that have not been directly studied is essentially an exercise in spatial prediction.

This project will study the interaction of management practices with different soil environments as a basis for understanding how results can transfer to other areas. By identifying and mapping the relationships between natural inherent soil factors and the impact of management practices, researchers aim to develop models capable of predicting soil nutrient outcomes from field-to-watershed scales. The challenge is to account for the factors contributing to soil variation.

Objective

Researchers will analyze how terrain variables induce variation within experimental plots, adding a multi-variate analysis component to an existing project on corn nitrogen needs under cereal rye cover crops. This knowledge will help identify which variables need to be considered in the development of transferable models that can provide a basis for scaling plot-scale research to field- and watershed-scales for optimal nutrient management. As such, this research lays the groundwork to expand development of spatial models to multiple fields with N-rate trials and over multiple years.

Approach

Remote sensing will provide efficient collection of data to serve as covariates for soil properties, such as fine resolution elevation data and multi-temporal, multi-spectral imagery.